A UAV Image and Geographic Data Integration Processing Method and Its Applications

17 - Arterial Tree Morphometry

It is difficult to measure the leaf area of plants by conventional methods as a result of the irregular leaf shapes. In this research, a new method was presented to measure leaf area using image processing techniques. The leaves images were acquired by MS3100 3CCD camera, and each image was composed of three channel data (green, red, near-infrared). The image data were transferred to a host computer and were stored as files in TIFF or JPEG format. Some image processing methods were applied to calibrate the leaf image, detect the margin of the leaf, and calculate the area by counting the pixels in the leaf. From the experimental results, it shows that the image method has good measurement accuracy; the relative error is less than 0.5 percent; and image processing is a rapid and non-destructive tool to measure the leaf area of plants.

Ahrmct-The use of a versatile digital image storage and processing system for scanning acoustic micrmcopy is described. In addition to the data capture and storage functions, different types of image processing functions of particular relevance to scanning acoustic microscopy have been incorporated These ipclude real-time grey scale enhancement and a variety of ofStine edge enhancement, spatial fdtering, and pseudocolor processing algorithms. These facilities can enhance particular aspects of the image or 6lter out unwanted artifacts. Typical results are presented. The further potential of digital processing for scanning acoustic microscopy is discussed. recurring problem associated with the use of imaging apparatus is the production of a display which will do justice to the quality of the data acquired and which is able to take advantage of the powerful electronic techniques for image storage and processing. The image processing described in this paper arose out of necessity, in the course of working with a relatively new form of imaging-scanning acoustic microscopy. Our approach has therefore been to present our results as an example of the application of general image processing techniques to a set of specific problems, arising from this form of microscopy. The scanning acoustic microscope (SAM) was first demonstrated by Quate and Lemons in 1973 [l]. Since then it has been developed sufficiently to allow its use as a tool for investigation of a wide range of materials as well as biological samples. It is particularly appropriate for the examination of the interior of optically opaque media [2] - [4]. In the basic operation of a SAM (reflection or transmission) [5], the object is immersed in a liquid (normally water) and place at the focal plane of an acoustic lens where it is interrogated by a focused ultrasonic beam. The object is subsequently scanned over the entire field of view in a raster manner so that the image is formed point by point. In a typical instrument the two orthogonal displacements of the scanner are measured by means of position encoders. Both amplitude and phase data may be recorded at each point in the scan. As the information and the position encoder outputs are produced in an analog form it was, until recently, accepted practice to resort

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.

Quantum image processing represents a pivotal field in quantum remote sensing[1-3]. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications[4-5], while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging[6-8]. Through systematic research and experiments[9-11], we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields[12-15]. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

This research explores the design and implementa- tion of an image and video processing platform using recon- figurable FPGA technology. With the increasing demand for real- time, high-performance multimedia processing in modern applications, a flexible and efficient solution is essential. The project utilizes an FPGA-based platform to handle image and video data processing through edge detection, image scaling, and real-time motion detection techniques. By integrating custom- built hardware components, this study aims to provide a low- power, high-throughput solution that meets the requirements of both image and video processing tasks. Materials and methods involved using Xilinx Virtex-II Pro FPGA for hardware imple- mentation, alongside embedded memory processors and multi- functional design elements such as zoom-in/out utilities. The system was evaluated through several experimental procedures, using various image and video datasets, to measure performance in terms of speed, resource utilization, and power consumption. Results indicated that the system achieved notable improvements in terms of efficiency, utilizing 25% of logic resources, 55% of on- chip memory, and 180mW of power. The discussion focuses on the adaptability of FPGA-based systems in meeting the diverse needs of multimedia processing, emphasizing scalability and low power consumption. In conclusion, the reconfigurable FPGA platform provides a versatile and efficient environment for image and video processing. The system’s resource efficiency, scalability, and potential for further enhancements make it a promising candidate for future multimedia applications. Keywords-Reconfigurable FPGA Architecture, Video and Image Processing, Edge Detection, Image Rescaling.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Currently, the fast evolution of cameras in recent years has made them promising tools for diagnostics of Tokamak. The solution presented in this paper consists of a prototype of high-speed visible image acquisition and processing system (HVIAPs) dedicated for experimental advanced superconducting tokamak shape and position control. Graphics processing unit (GPU) and field-programmable gate array (FPGA) are typically used as accelerators or co-processors in addition to a CPU. Such a heterogeneous computing system can combine the advantages of its individual components. The main imaging equipment components used for this system are four high-resolution fast cameras equipped with fiber interface. Image data from the cameras are received by the frame grabber card based on the FPGA and transmitted to the GPU via the peripheral component interconnect express interface. The software support for the system includes low-level drivers and application programming interface libraries for all components and the algorithm developed for image processing. The offline image process results are compared to equilibrium fitting code, which is a commonly used reconstruction method, with an average error of 1.5 cm. The total processing time for one frame is less than 0.3 ms.

Ultrafast X-ray tomographic imaging of multiphase flow in bubble columns - Part 1: Image processing and reconstruction comparison

Land use and urban development surveys involve the interpretation of a large volume of data coming from satellite images processing as well as from remote sensors networks. In order to facilitate this interpretation, the development of a multipurpose Intelligent Data Analysis (IDA) framework for supporting geographical data perception is proposed here. The framework makes use of semantic technologies and relies on a novel knowledge model composed by a foundational ontology (DOLCE Ultra-Lite, also called DUL), three core reference ontologies (the Temporal Abstraction Ontology or TAO, the Semantic Sensor Network ontology or SSN and the SWRL Temporal Ontology or SWRLTO) and two specific domain ontologies (the Urban Ontology or URO and the Geographic Data ontology or GeoD, developed by our team). They play different and well specific roles in the whole process of perception. The paper shows how to apply SSN to manage measurements of geographical regions provided by satellite images processing software. In a similar way, TAO has been extended to deal with the abstractions resulting from geographical data interpretation. An example shows a SWRL based implementation of a perception process that gradually abstracts geographical features and objects.

Predictive selection rule of favourable image processing methods for X-ray micro-computed tomography images of tablets

In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.